Brain Imaging and Behavior

, Volume 9, Issue 4, pp 854–867 | Cite as

Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI

  • Matthew Evan Magnuson
  • Garth John Thompson
  • Hillary Schwarb
  • Wen-Ju Pan
  • Andy McKinley
  • Eric H. Schumacher
  • Shella Dawn Keilholz
Original Research


The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.


Cognitive processing High temporal resolution fMRI Resting state Default mode network Task positive network Working memory Interrupter task 



psychomotor vigilance task


symmetry span task


operation span task


Raven’s advanced progressive matrices


default mode network


task positive network

Functional network

functionally connected network



Funding was provided in part by the Bio-nano-enabled Inorganic/Organic Nanostructures and Improved Cognition (BIONIC) Air Force Center of Excellence at the Georgia Institute of Technology. This research was also partially funded under an appointment to the U.S. Department of Homeland Security (DHS) Scholarship and Fellowship Program, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and DHS (contract number DE-AC05-06OR23100). All opinions expressed in this paper are the author’s and do not necessarily reflect the policies and views of DHS, DOE, or ORAU/ORISE. We would also like to thank Dr. Waqas Majeed for his suggestions regarding data preprocessing, Nytavia Wallace for her assistance with data collection, and Brian Roberts for his insightful discussions.

Conflict of interest

Matthew Evan Magnuson, Garth John Thompson, Hillary Schwarb, Wen-Ju Pan, Andy McKinley, Eric H. Schumacher, and Shella Dawn Keilholz declare that they have no conflicts of interest.

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.


  1. Albert, N. B., Robertson, E. M., & Miall, R. C. (2009). The resting human brain and motor learning. Current Biology, 19, 1023–7.PubMedCentralCrossRefPubMedGoogle Scholar
  2. Baddeley, A. D., & Logie, R. H. (1999). Working memory: The multiple-component model. New York: Cambridge University Press.Google Scholar
  3. Biswal, B., et al. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34, 537–41.CrossRefPubMedGoogle Scholar
  4. Boly, M., et al. (2007). Baseline brain activity fluctuations predict somatosensory perception in humans. Proceedings of the National Academy of Sciences of the United States of America, 104, 12187–92.PubMedCentralCrossRefPubMedGoogle Scholar
  5. Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14, 277–90.CrossRefPubMedGoogle Scholar
  6. Brookes, M. J., et al. (2011). Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. NeuroImage, 56, 1082–1104.PubMedCentralCrossRefPubMedGoogle Scholar
  7. Carlson, G. C., & Coulter, D. A. (2008). In vitro functional imaging in brain slices using fast voltage-sensitive dye imaging combined with whole-cell patch recording. Nature Protocols, 3, 249–55.PubMedCentralCrossRefPubMedGoogle Scholar
  8. Carvajal-Rodriguez, A., de Una-Alvarez, J., & Rolan-Alvarez, E. (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics, 10, 209.PubMedCentralCrossRefPubMedGoogle Scholar
  9. Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50, 81–98.PubMedCentralCrossRefPubMedGoogle Scholar
  10. Cordes, D., et al. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR. American Journal of Neuroradiology, 21, 1636–44.PubMedGoogle Scholar
  11. Dinges, D. F., & Powell, J. W. (1985). Microcomputer analyses of performance on a portable, simple visual Rt task during sustained operations. Behavior Research Methods, Instruments, & Computers, 17, 652–655.CrossRefGoogle Scholar
  12. Eichele, T., et al. (2008). Prediction of human errors by maladaptive changes in event-related brain networks. Proceedings of the National Academy of Sciences of the United States of America, 105, 6173–8.PubMedCentralCrossRefPubMedGoogle Scholar
  13. Engle, R. W. (2007). What is working-memory capacity? Washington: American Psychological Association.Google Scholar
  14. Evers, E. A., et al. (2012). The effects of sustained cognitive task performance on subsequent resting state functional connectivity in healthy young and middle-aged male schoolteachers. Brain Connectivity, 2, 102–12.CrossRefPubMedGoogle Scholar
  15. Fox, M. D., et al. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 9673–8.PubMedCentralCrossRefPubMedGoogle Scholar
  16. Fox, M. D., et al. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences of the United States of America, 103, 10046–51.PubMedCentralCrossRefPubMedGoogle Scholar
  17. Fox, M. D., et al. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101, 3270–3283.PubMedCentralCrossRefPubMedGoogle Scholar
  18. Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26, 15–29.CrossRefPubMedGoogle Scholar
  19. Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9, 474–80.CrossRefPubMedGoogle Scholar
  20. Fuster, J. M. (2000). The module: Crisis of a paradigm. Neuron, 26, 51–53.CrossRefGoogle Scholar
  21. Garrity, A. G., et al. (2007). Aberrant “default mode” functional connectivity in schizophrenia. The American Journal of Psychiatry, 164, 450–7.CrossRefPubMedGoogle Scholar
  22. Gavrilescu, M., et al. (2002). Simulation of the effects of global normalization procedures in functional MRI. NeuroImage, 17, 532–542.CrossRefPubMedGoogle Scholar
  23. Grady, C. L., et al. (2001). Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease. Brain, 124, 739–56.CrossRefPubMedGoogle Scholar
  24. Greicius, M. D., & Menon, V. (2004). Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. Journal of Cognitive Neuroscience, 16, 1484–92.CrossRefPubMedGoogle Scholar
  25. Greicius, M. D., et al. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62, 429–37.PubMedCentralCrossRefPubMedGoogle Scholar
  26. Hampson, M., et al. (2002). Detection of functional connectivity using temporal correlations in MR images. Human Brain Mapping, 15, 247–62.CrossRefPubMedGoogle Scholar
  27. Hampson, M., et al. (2006). Brain connectivity related to working memory performance. The Journal of Neuroscience, 26, 13338–43.PubMedCentralCrossRefPubMedGoogle Scholar
  28. He, B. J., et al. (2008). Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture. Proceedings of the National Academy of Sciences of the United States of America, 105, 16039–44.PubMedCentralCrossRefPubMedGoogle Scholar
  29. Hesselmann, G., et al. (2008). Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proceedings of the National Academy of Sciences of the United States of America, 105, 10984–9.PubMedCentralCrossRefPubMedGoogle Scholar
  30. Hlinka, J., et al. (2010). Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: An inter-subject analysis. NeuroImage, 53, 239–46.CrossRefPubMedGoogle Scholar
  31. Honey, C. J., et al. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104, 10240–5.PubMedCentralCrossRefPubMedGoogle Scholar
  32. Hutchison, R. M., et al. (2013). Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping, 34, 2154–77.CrossRefPubMedGoogle Scholar
  33. Jaeggi, S. M., et al. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105, 6829–33.PubMedCentralCrossRefPubMedGoogle Scholar
  34. Jelles, B., et al. (2008). Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clinical Neurophysiology, 119, 837–41.CrossRefPubMedGoogle Scholar
  35. Kane, M. J., et al. (2007). Working memory, attention control, and the N-back task: a question of construct validity. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33, 615–22.CrossRefPubMedGoogle Scholar
  36. Keilholz, S., et al. (2013). Dynamic properties of functional connectivity in the rodent. Brain Connectivity, 3, 31–40.PubMedCentralCrossRefPubMedGoogle Scholar
  37. Kelly, A. M., et al. (2008). Competition between functional brain networks mediates behavioral variability. NeuroImage, 39, 527–37.CrossRefPubMedGoogle Scholar
  38. Lancaster, J. L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–31.CrossRefPubMedGoogle Scholar
  39. Liu, Y., et al. (2007). Whole brain functional connectivity in the early blind. Brain, 130, 2085–96.CrossRefPubMedGoogle Scholar
  40. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869–78.CrossRefPubMedGoogle Scholar
  41. Lowe, M. J., et al. (2002). Multiple sclerosis: Low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity initial results. Radiology, 224, 184–92.CrossRefPubMedGoogle Scholar
  42. Majeed, W., et al. (2011). Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. NeuroImage, 54, 1140–1150.PubMedCentralCrossRefPubMedGoogle Scholar
  43. Moeller, S., et al. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Res Med, 63(5), 1144–53.CrossRefGoogle Scholar
  44. Nir, Y., et al. (2008). Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nature Neuroscience, 11, 1100–8.PubMedCentralCrossRefPubMedGoogle Scholar
  45. Ogawa, S., et al. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, 9868–72.PubMedCentralCrossRefPubMedGoogle Scholar
  46. Pan, W. J., et al. (2011). Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connectivity, 1, 119–31.PubMedCentralCrossRefPubMedGoogle Scholar
  47. Polli, F. E., et al. (2005). Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission. Proceedings of the National Academy of Sciences of the United States of America, 102, 15700–15705.PubMedCentralCrossRefPubMedGoogle Scholar
  48. Prabhakaran, V., et al. (1997). Neural substrates of fluid reasoning: an fMRI study of neocortical activation during performance of the Raven’s progressive matrices test. Cognitive Psychology, 33, 43–63.CrossRefPubMedGoogle Scholar
  49. Prado, J., & Weissman, D. H. (2011). Heightened interactions between a key default-mode region and a key task-positive region are linked to suboptimal current performance but to enhanced future performance. NeuroImage, 56, 2276–82.CrossRefPubMedGoogle Scholar
  50. Raichle, M. E., et al. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98, 676–82.PubMedCentralCrossRefPubMedGoogle Scholar
  51. Raven, J. (2000). The Raven’s progressive matrices: change and stability over culture and time. Cognitive Psychology, 41, 1–48.CrossRefPubMedGoogle Scholar
  52. Sadaghiani, S., Hesselmann, G., & Kleinschmidt, A. (2009). Distributed and antagonistic contributions of ongoing activity fluctuations to auditory stimulus detection. The Journal of Neuroscience, 29, 13410–7.CrossRefPubMedGoogle Scholar
  53. Sadaghiani, S., et al. (2010). The relation of ongoing brain activity, evoked neural responses, and cognition. Frontiers in Systems Neuroscience, 4, 20.PubMedCentralPubMedGoogle Scholar
  54. Sakoglu, U., et al. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Magma, 23, 351–66.PubMedCentralCrossRefPubMedGoogle Scholar
  55. Sala-Llonch, R., et al. (2012). Brain connectivity during resting state and subsequent working memory task predicts behavioural performance. Cortex, 48, 1187–96.CrossRefPubMedGoogle Scholar
  56. Seeley, W. W., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27, 2349–56.PubMedCentralCrossRefPubMedGoogle Scholar
  57. Shipstead, Z., et al. (2012). The scope and control of attention as separate aspects of working memory. Memory, 20, 608–628.CrossRefPubMedGoogle Scholar
  58. Shmuel, A., & Leopold, D. A. (2008). Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest. Human Brain Mapping, 29, 751–61.CrossRefPubMedGoogle Scholar
  59. Sonuga-Barke, E. J., & Castellanos, F. X. (2007). Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience and Biobehavioral Reviews, 31(7), 977–86.CrossRefPubMedGoogle Scholar
  60. Stevens, A. A., et al. (2012). Functional brain network modularity captures inter- and intra-individual variation in working memory capacity. PloS One, 7, e30468.PubMedCentralCrossRefPubMedGoogle Scholar
  61. Tambini, A., Ketz, N., & Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron, 65, 280–90.PubMedCentralCrossRefPubMedGoogle Scholar
  62. Thompson, G. J., et al. (2013). Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually. Human Brain Mapping, 34, 3280–98.CrossRefPubMedGoogle Scholar
  63. Tzourio-Mazoyer, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–89.CrossRefPubMedGoogle Scholar
  64. Uddin, L. Q., et al. (2009). Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Human Brain Mapping, 30, 625–37.CrossRefPubMedGoogle Scholar
  65. Unsworth, N., et al. (2005). An automated version of the operation span task. Behavior Research Methods, 37, 498–505.CrossRefPubMedGoogle Scholar
  66. van den Heuvel, M. P., et al. (2009). Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience, 29, 7619–24.CrossRefPubMedGoogle Scholar
  67. Villalobos, M. E., et al. (2005). Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. NeuroImage, 25, 916–25.PubMedCentralCrossRefPubMedGoogle Scholar
  68. Waites, A. B., et al. (2005). Effect of prior cognitive state on resting state networks measured with functional connectivity. Human Brain Mapping, 24, 59–68.CrossRefPubMedGoogle Scholar
  69. Weissman, D. H., et al. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9, 971–8.CrossRefPubMedGoogle Scholar
  70. Xu, X., et al. (2010). High precision and fast functional mapping of cortical circuitry through a novel combination of voltage sensitive dye imaging and laser scanning photostimulation. Journal of Neurophysiology, 103, 2301–12.PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Matthew Evan Magnuson
    • 1
  • Garth John Thompson
    • 1
  • Hillary Schwarb
    • 2
  • Wen-Ju Pan
    • 1
  • Andy McKinley
    • 3
  • Eric H. Schumacher
    • 2
  • Shella Dawn Keilholz
    • 1
  1. 1.Georgia Institute of Technology and Biomedical EngineeringEmory UniversityAtlantaUSA
  2. 2.Georgia Institute of Technology School of PsychologyAtlantaUSA
  3. 3.Air Force Research Laboratory Wright-Patterson Air Force BaseAtlantaUSA

Personalised recommendations